{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、对连续型特征，可以用哪个函数可视化其分布？（给出你最常用的一个即可），并根据代码运行结果给出示例。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 答：对于连续型特征，通常采用seaborn库里面的distplot函数可视化其分布，代码示例如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f87fd8cf3c8>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv(\"boston_housing.csv\")  # 导入数据集\n",
    "\n",
    "sns.distplot(df['MEDV'], bins=30, kde=True)  # 运用distplot函数可视化“MEDV”的分布情况\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、对两个连续型特征，可以用哪个函数得到这两个特征之间的相关性？根据代码运行结果，给出示例。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 答：对两个连续型特征，可以采用DataFrame的corr()方法计算这两个特征之间的相关性。代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f87fd86be80>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(13,9))\n",
    "data_corr = df.corr()  # 采用corr（）函数求两个特征之间的相关性\n",
    "sns.heatmap(data_corr, annot=True)  # 用热力图将相关性呈现出来"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、如果发现特征之间有较强的相关性，在选择线性回归模型时应采取什么措施？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 答：如果发现特征之间有较强的相关性，可以考虑在特征层面上进行PCA降维处理，在模型层面可以加正则项。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 四、当采用代正则项的模型以及采用随机梯度下降优化算法时，需要对输入（连续型）特征进行去量纲预处理。课程代码给出了用标准化（StandardScaler）的结果，请改成最小最大缩放（MinMaxScaler）去量纲，并重新训练最小二乘线性回归、岭回归和Lasso模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/preprocessing/data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "# 采用最小最大缩放 MinMaxScaler 去量纲\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 从原始数据中分离输入特征X和输出y\n",
    "y = df['MEDV']\n",
    "X = df.drop('MEDV', axis = 1)\n",
    "\n",
    "feat_names = X.columns\n",
    "\n",
    "# 分别初始化对特征和目标的标准化器\n",
    "ss_X = MinMaxScaler()\n",
    "ss_y = MinMaxScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "# 对训练数据，先调用fit方法训练模型，得到模型参数,然后对训练数据和测试数据进行transform\n",
    "X = ss_X.fit_transform(X)\n",
    "y = ss_y.fit_transform(y.values.reshape(-1, 1))  # 这里直接y.reshape(-1, 1)行不通，必须借用values调用reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存特征工程的结果到文件，供机器学习模型使用\n",
    "fe_data = pd.DataFrame(data = X, columns = feat_names, index = df.index)\n",
    "fe_data['MEDV'] = y\n",
    "\n",
    "fe_data.to_csv('FE_boston_housing_第四周基础作业.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.067815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>0.577505</td>\n",
       "      <td>0.641607</td>\n",
       "      <td>0.269203</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.208015</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089680</td>\n",
       "      <td>0.422222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.547998</td>\n",
       "      <td>0.782698</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.204470</td>\n",
       "      <td>0.368889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.694386</td>\n",
       "      <td>0.599382</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.989737</td>\n",
       "      <td>0.063466</td>\n",
       "      <td>0.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.658555</td>\n",
       "      <td>0.441813</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.994276</td>\n",
       "      <td>0.033389</td>\n",
       "      <td>0.631111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000705</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.687105</td>\n",
       "      <td>0.528321</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.099338</td>\n",
       "      <td>0.693333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CRIM    ZN     INDUS  CHAS       NOX        RM       AGE       DIS  \\\n",
       "0  0.000000  0.18  0.067815   0.0  0.314815  0.577505  0.641607  0.269203   \n",
       "1  0.000236  0.00  0.242302   0.0  0.172840  0.547998  0.782698  0.348962   \n",
       "2  0.000236  0.00  0.242302   0.0  0.172840  0.694386  0.599382  0.348962   \n",
       "3  0.000293  0.00  0.063050   0.0  0.150206  0.658555  0.441813  0.448545   \n",
       "4  0.000705  0.00  0.063050   0.0  0.150206  0.687105  0.528321  0.448545   \n",
       "\n",
       "        RAD       TAX  PTRATIO         B     LSTAT      MEDV  \n",
       "0  0.000000  0.208015      0.3  1.000000  0.089680  0.422222  \n",
       "1  0.043478  0.104962      0.5  1.000000  0.204470  0.368889  \n",
       "2  0.043478  0.104962      0.5  0.989737  0.063466  0.660000  \n",
       "3  0.086957  0.066794      0.6  0.994276  0.033389  0.631111  \n",
       "4  0.086957  0.066794      0.6  1.000000  0.099338  0.693333  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fe_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# 图形出现在Notebook里面而不是窗口\n",
    "%matplotlib inline  \n",
    "# 读取做完特征工程后的数据\n",
    "df = pd.read_csv('FE_boston_housing_第四周基础作业.csv')\n",
    "\n",
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = df['MEDV']\n",
    "X = df.drop(['MEDV'], axis=1)\n",
    "\n",
    "# 特征名称，用于后续显示权重系数对应的特征\n",
    "feat_names = X.columns\n",
    "\n",
    "# 将数据分割训练数据和测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练最小二乘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.6902959550898358\n",
      "The r2 score of LinearRegression on train is 0.7451448367308489\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 1.使用默认配置初始化学习器实例\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 2.用训练数据训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 3.用训练好的模型对测试集进行预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "# 4.使用r2_score评价模型在测试集和训练集上的性能，并输出评价结果\n",
    "from sklearn.metrics import r2_score\n",
    "#测试集\n",
    "print(\"The r2 score of LinearRegression on test is\", r2_score(y_test, y_test_pred_lr))\n",
    "#训练集\n",
    "print(\"The r2 score of LinearRegression on train is\", r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.6929146520257345\n",
      "The r2 score of RidgeCV on train is 0.7450810988477314\n"
     ]
    }
   ],
   "source": [
    "# 带有广义交叉项的岭回归\n",
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "# 1.设置超参数（正则参数）范围\n",
    "# alphas = [0.01, 0.1, 1, 10, 100]\n",
    "\n",
    "# 2.生成一个RidgeCV实例\n",
    "# ridge = RidgeCV(alphas=alphas, store_cv_values=True)\n",
    "ridge = RidgeCV()\n",
    "\n",
    "# 3.训练模型\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "# 4.预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "# 5.使用r2_score评价模型在测试集和训练集上的性能，并输出评价结果\n",
    "# from sklearn.metrics import r2_score\n",
    "#测试集\n",
    "print(\"The r2 score of RidgeCV on test is\", r2_score(y_test, y_test_pred_ridge))\n",
    "#训练集\n",
    "print(\"The r2 score of RidgeCV on train is\", r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练Lasso模型 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.6909712241255915\n",
      "The r2 score of LassoCV on train is 0.7451229340174219\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/model_selection/_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "# 1.设置超参数（正则参数）范围\n",
    "# alphas = [0.01, 0.1, 1, 10, 100]\n",
    "\n",
    "# 2.生成一个RidgeCV实例\n",
    "# lasso = LassoCV(alphas=alphas)\n",
    "lasso = LassoCV()\n",
    "\n",
    "# 3.训练模型\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "# 4.预测\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "# 5.使用r2_score评价模型在测试集和训练集上的性能，并输出评价结果\n",
    "# from sklearn.metrics import r2_score\n",
    "#测试集\n",
    "print(\"The r2 score of LassoCV on test is\", r2_score(y_test, y_test_pred_lasso))\n",
    "#训练集\n",
    "print(\"The r2 score of LassoCV on train is\", r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 五、代码中给出了岭回归（RidgeCV）和Lasso（LassoCV）超参数（alpha_）调优的过程，请结合两个最佳模型以及最小二乘线性回归模型的结果，给出什么场合应该用岭回归，什么场合用Lasso，什么场合用最小二乘。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答：由上题cell单元中的运行结果：\n",
    "1. 最小二乘线性回归结果\n",
    "The r2 score of LinearRegression on test is 0.6902959550898358\n",
    "The r2 score of LinearRegression on train is 0.7451448367308489\n",
    "根据最小二乘线性回归的解$(X^TX)^{-1}X^TY$，需要对矩阵$X^TX$求逆。当输入特征存在共线性（某些特征可以用其他特征的线性组合表示），矩阵X是接近不满秩，矩阵$X^TX$接近奇异，求逆不稳定，所以结果不会很理想。所以最小二乘线性回归适用于特征之间不存在共线性的情况。由于最小二乘与其他两个模型相比未加正则项，只考虑了对训练样本的拟合程度，故在训练集上的结果最好。\n",
    "2. 岭回归模型评价结果如下：\n",
    "The r2 score of RidgeCV on test is 0.6929146520257345\n",
    "The r2 score of RidgeCV on train is 0.7450810988477314\n",
    "从结果来看，岭回归虽然在训练集上的效果不如最小二乘线性回归，但在测试集上的效果却要更好一些。岭回归由于加入了L2正则项，通过alpha参数的调节，可以平衡拟合程度和模型复杂度之间的关系，岭回归的解为：$(X^TX+\\lambda$$I)^{-1}X^TY$即使在输入特征存在共线性，矩阵X是接近不满秩，矩阵$X^TX$对角线存在等于0或接近于0的元素，但$0+\\lambda\\neq0$，$(X^TX+\\lambda$$I)$求逆仍可得到稳定解。因此岭回归适用于输入特征存在共线性的情况。\n",
    "3. Lasso回归模型评价结果如下：\n",
    "The r2 score of LassoCV on test is 0.6909712241255915\n",
    "The r2 score of LassoCV on train is 0.7451229340174219\n",
    "从结果来看，Lasso回归模型同样在训练集上的效果不如最小二乘线性回归，但在测试集上的效果却要更好一些，但在此特征下不如岭回归。Lasso回归中的L1正则同岭回归的L2正则一样是用来平衡拟合程度和模型复杂度的，但当正则参数取合适值时，L1正则能使有些线性回归系数为0,得到稀疏模型。所以，Lasso回归模型适用于，当输入特征多，有些特征与目标变量之间相关性很弱时，L1正则可能只选择强相关的特征，模型解释性好。"
   ]
  }
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